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Visualizing loan approval yield

In the realm of financial services, understanding the factors that influence loan approval rates is crucial for both lenders and borrowers. A financial institution has conducted a study and collected data on loan applications, detailing the amount requested, the applicant's credit score, employment status, and the ultimate yield of the approval process. This rich dataset offers a window into the nuanced dynamics at play in loan decision-making. You have been asked to dive into the loan_approval_yield dataset to understand how loan amounts and credit scores influence approval yields.

The loan_approval_yield DataFrame, seaborn as sns, and matplotlib.pyplot as plt have been loaded for you.

Это упражнение является частью курса

Experimental Design in Python

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# Use Seaborn to create the bar graph
sns.catplot(x="____", 
            y="____", 
            hue="____", 
            kind="____", 
            data=loan_approval_yield)
plt.title("Loan Approval Yield by Amount and Credit Score")
plt.show()
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